Lithium battery thermal management regulation and control method, device and equipment

Abstract

The invention relates to the technical field of lithium battery thermal management, and discloses a lithium battery thermal management regulation and control method, device and equipment and a computer program product, and the method comprises the steps: collecting the temperature data of a lithium battery cell in real time through a plurality of embedded temperature sensors, and forming a temperature matrix; calculating heat flux density between the battery cells based on a Fourier heat conduction equation to generate a heat flux matrix; forming a cooling demand parameter matrix; and dynamically adjusting the flow velocity of the cooling liquid based on a particle swarm optimization algorithm, and automatically adjusting the flow velocity of the cooling liquid through a closed-loop feedback mechanism. Compared with the prior art that performance reduction or service life shortening of a lithium battery system is easily caused by overheating under high-power use or extreme environment conditions, and particularly, effective and uniform heat dissipation cannot be realized when the temperature difference of battery cells is relatively large, accurate heat management is realized by combining temperature real-time monitoring and adjustment of the flow rate of cooling liquid, and the service life of the lithium battery system is prolonged. Therefore, the problem of overheating or non-uniform local cooling is avoided, and the stability and safety of the battery system are improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of lithium battery thermal management, and in particular relates to a lithium battery thermal management control method, device and equipment. Background Art

[0002] At present, the thermal management technology of lithium batteries has certain shortcomings. For example, in the existing technology, under high-power discharge or high-temperature environment, the thermal management system of lithium batteries often finds it difficult to evenly control the temperature of the battery cells, resulting in overheating or insufficient cooling of some battery cells. This uneven thermal management will not only affect the overall performance of the battery, but may also reduce the service life of the battery, and in extreme cases may cause safety hazards. The existing technology cannot fully meet the needs of precise thermal management of lithium batteries under multiple working conditions and multiple environmental conditions. For example, the conventional cooling system lacks a rapid response mechanism to dynamic temperature changes, resulting in the inability to effectively respond to the rapid rise in battery cell temperature. At the same time, most traditional cooling flow control methods are based on fixed parameter adjustment and cannot be intelligently adjusted according to the actual heat demand of the battery cell. Therefore, there is an urgent need for a thermal management method that can still achieve precise temperature control of each battery cell of a lithium battery under a complex working environment. While ensuring temperature balance and heat dissipation efficiency, it can also dynamically respond to temperature changes in the battery system to improve the safety, service life and overall performance of the battery. Summary of the invention

[0003] In view of the above-mentioned technical deficiencies, the purpose of the present invention is to propose a lithium battery thermal management and control method, aiming to solve the technical problem in the prior art that the lithium battery system is easily degraded in performance or shortened in life due to overheating under high power use or extreme environmental conditions, especially when the temperature difference of the battery cells is large, and effective and uniform heat dissipation cannot be achieved.

[0004] In order to solve the above technical problems, the present invention adopts the following technical solution: The present invention provides a lithium battery thermal management control method,

[0005] The lithium battery thermal management control method comprises:

[0006] Step S10: Based on multiple embedded temperature sensors, the temperature data of the lithium battery cells are collected in real time to form a temperature matrix T. The accuracy of the temperature sensors is ±0.1°C, and the temperature collection frequency is 10 times per second.

[0007] Step S20: According to the temperature matrix T, the heat flux density q between the cells is calculated by the Fourier heat conduction equation, and then the heat flux matrix H is generated, wherein the calculation formula of the heat flux density q is:

[0008]

[0009] Where q is the heat flux density, k is the thermal conductivity of the lithium battery material, is the temperature gradient;

[0010] Step S30: Based on the heat flow matrix H, the cooling requirement of each battery cell is calculated using a differential equation to form a cooling requirement parameter matrix C. The differential equation is:

[0011]

[0012] Where T i is the temperature of the ith cell, T avg is the average temperature of the battery pack, R c is the heat capacity coefficient of the cooling fluid;

[0013] Step S40: According to the cooling demand parameter matrix C, the flow rate of the coolant is dynamically adjusted by the particle swarm optimization algorithm. The optimization goal is to control the temperature difference of each battery cell within ±2°C, the flow rate range of the coolant is 50 to 200 liters per minute, and the flow rate adjustment range is 1 to 5 m / s;

[0014] Step S50: Monitor the deviation between the temperature data after cooling and the optimization target in real time through closed-loop feedback. When the temperature difference exceeds 1°C, automatically adjust the flow rate of the coolant.

[0015] Preferably, in step S40, the particle swarm optimization algorithm adopts the following formula:

[0016] v i (t+1)=wv i (t)+c1r1(p i,best -x i (t))+c2r2(g best -x i (t))

[0017] Among them, v i (t+1) represents the velocity of particle i at time t+1, v i (t) represents the velocity of particle i at time t, x i (t) is the current position of particle i, p i,best is the historical optimal position of particle i, g best is the global optimal position, c1 and c2 are learning factors, r1 and r2 are random numbers, and w is the inertia weight.

[0018] Preferably, in step S40, the particle swarm optimization algorithm optimizes the target using the formula:

[0019]

[0020] Among them, T i is the temperature of the ith cell, T avgis the average temperature of the battery system, and n is the total number of battery cells.

[0021] Preferably, in step S50, the flow rate of the coolant is automatically adjusted using the formula:

[0022] V cooland (t+1)=V cooland (t)+α·Δm

[0023] Where V cooland (t+1) is the coolant flow rate at time t+1, V cooland (t) is the coolant flow rate at the current time t, α is the adaptive adjustment coefficient, which determines the amplitude of the flow rate adjustment, and Δm is the deviation between the current battery temperature and the target temperature.

[0024] Preferably, in step S10, the embedded temperature sensor is a high-precision digital temperature sensor.

[0025] Preferably, in step S10, the temperature collection range is -20°C to 80°C.

[0026] Preferably, in step S50, the response time of the closed-loop feedback is less than 3 seconds.

[0027] The present invention also provides a lithium battery thermal management device, comprising:

[0028] A temperature acquisition module is used to collect temperature data of lithium battery cells in real time based on multiple embedded temperature sensors to form a temperature matrix T. The accuracy of the temperature sensor is ±0.1°C, and the temperature acquisition frequency is 10 times per second;

[0029] The heat flow calculation module is used to calculate the heat flow density q between the cells according to the temperature matrix T through the Fourier heat conduction equation, and then generate the heat flow matrix H;

[0030] A cooling demand calculation module is used to calculate the cooling demand of each battery cell based on the heat flow matrix H using a differential equation to form a cooling demand parameter matrix C;

[0031] The cooling flow rate adjustment module is used to dynamically adjust the flow rate of the coolant according to the cooling demand parameter matrix C through the particle swarm optimization algorithm. The optimization goal is to control the temperature difference of each battery cell within ±2°C. The flow rate range of the coolant is 50 to 200 liters per minute, and the flow rate adjustment range is 1 to 5 meters per second.

[0032] The feedback control module is used to monitor the deviation between the temperature data after cooling and the optimization target in real time through closed-loop feedback. When the temperature difference exceeds 1°C, the flow rate of the coolant is automatically adjusted;

[0033] The present invention also provides a lithium battery thermal management device, comprising:

[0034] A memory, a processor, and a lithium battery thermal management program stored in the memory and executable on the processor, wherein the lithium battery thermal management program implements the lithium battery thermal management control method as described above when executed by the processor.

[0035] The present invention also provides a computer program product, including a lithium battery thermal management program, which implements the lithium battery thermal management control method when executed by a processor.

[0036] The beneficial effect of the present invention is that compared with the prior art, when the lithium battery system is used at high power or in extreme environmental conditions, it is easy to cause performance degradation or shortened life due to overheating, especially when the temperature difference of the battery cells is large, and effective and uniform heat dissipation cannot be achieved. The present invention realizes precise thermal management by adjusting the coolant flow rate in combination with real-time temperature monitoring, thereby avoiding the problem of overheating or uneven local cooling and improving the stability and safety of the battery system. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0038] Figure 1 The figure is a flow chart of a first embodiment of a lithium battery thermal management control method of the present invention.

[0039] Figure 2 The present invention is a schematic diagram of a device for a lithium battery thermal management control method. DETAILED DESCRIPTION

[0040] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0041] Embodiment 1: Figure 1 , which is a flow chart of the first embodiment of the thermal management and control method for a lithium battery of the present invention, and provides the first embodiment of the thermal management and control method for a lithium battery of the present invention.

[0042] Step S10: Based on multiple embedded temperature sensors, the temperature data of the lithium battery cells are collected in real time to form a temperature matrix T. The accuracy of the temperature sensors is ±0.1°C, and the temperature collection frequency is 10 times per second.

[0043] It should be noted that the temperature sensors are evenly distributed at each cell position in the battery pack to ensure that the temperature changes of each cell can be monitored in real time. These temperature sensors can not only collect temperature data with high accuracy, but also respond quickly when the cell temperature changes suddenly, ensuring the real-time nature of the data.

[0044] It can be understood that the temperature matrix T is a two-dimensional matrix, in which each element represents the cell temperature measured at a certain moment. This matrix will be used for heat flow calculation and cooling demand assessment in subsequent steps. In this way, it can ensure that the temperature distribution of each cell in the battery pack is effectively monitored.

[0045] It should be understood that although the arrangement and acquisition frequency of the temperature sensor are specifically limited to 10 times per second in the present embodiment, in actual applications, the acquisition frequency of the temperature sensor can be appropriately adjusted according to different battery system scales and power output requirements. For example, the acquisition frequency can be set to 1 to 20 times per second.

[0046] For example, a lithium battery pack consists of 100 cells, each of which is embedded with a high-precision temperature sensor. By collecting the temperature data of each cell in real time, a 100×n temperature matrix T is formed (n is the number of sampling times in the time series). Assuming the acquisition frequency is 10 times per second, a 100×6000 temperature matrix will be generated after running for 10 minutes for subsequent heat flow analysis and cooling demand calculation.

[0047] Step S20: According to the temperature matrix T, the heat flux density q between the cells is calculated by the Fourier heat conduction equation, and then the heat flux matrix H is generated, wherein the calculation formula of the heat flux density q is:

[0048]

[0049] Where q is the heat flux density, k is the thermal conductivity of the lithium battery material, which ranges from 1.5 to 2.5 W / m·K. is the temperature gradient;

[0050] It should be understood that the calculation of heat flux density q is based on the temperature matrix T and the thermal conductivity k of the material, which can help accurately evaluate the heat flux distribution between lithium battery cells; the temperature gradient of the cell It reflects the temperature difference between different cells inside the battery, thus affecting the conduction direction and intensity of heat flow.

[0051] It should be understood that in practical applications, the thermal conductivity k of lithium battery materials is not a fixed value, but is dynamically affected by temperature, material type and battery load. Therefore, the present invention improves the traditional Fourier heat conduction equation and introduces a dynamic adjustment mechanism for thermal conductivity changes with temperature:

[0052] k(T)=k0·(1+β·(TT ref ))

[0053] Where k(T) is the thermal conductivity function value of the lithium battery material, and k0 is the reference temperature T ref The initial thermal conductivity under ; β is the influence coefficient of temperature on thermal conductivity; T is the battery core temperature.

[0054] For example, suppose that in a lithium battery system, the thermal conductivity k0 at the reference temperature T ref = 1.8W / m·K at 25℃, and the temperature influence coefficient β is 0.01. When the core temperature T rises to 40℃, the calculation result of the thermal conductivity k(T) is: k(40) = 1.8×(1+0.01×(40-25)) = 1.8×(1+0.15) = 2.07W / mK

[0055] Step S30: Based on the heat flow matrix H, the cooling requirement of each battery cell is calculated using a differential equation to form a cooling requirement parameter matrix C. The differential equation is:

[0056]

[0057] Where T i is the temperature of the ith cell, T avg is the average temperature of the battery pack, R c is the heat capacity coefficient of the cooling fluid, ranging from 0.9 to 1.1 J / g·K;

[0058] It should be noted that the calculation of cooling requirements depends not only on the temperature difference of the battery cells, but also on the heat capacity coefficient R of the cooling fluid. c By introducing R c The system can more accurately evaluate the actual cooling requirements of the battery cells under different fluid and temperature conditions, making the cooling system more flexible in response.

[0059] Understandably, T i -T avg It reflects the difference between the temperature of the ith cell and the average temperature of the battery pack. A larger temperature difference means that the cell needs stronger cooling. c Determines the ability of the cooling fluid to absorb heat. The higher the value, the more effectively the fluid can absorb heat.

[0060] It should be understood that in order to better achieve uniform cooling management of the battery pack, in addition to evaluating the cooling demand through temperature difference, the type and characteristics of the cooling fluid (i.e., R c For example, the heat capacity coefficient of different coolants may change in different temperature ranges, so the present invention particularly considers the effect of the change in the heat capacity of the fluid on the cooling demand when using the differential equation.

[0061] For example, suppose a cell T i The temperature is 50°C, and the average temperature of the battery pack is T avg At 45°C, the cooling fluid R c is 1.0J / g·K. Then the cooling requirement of the battery cell is calculated as:

[0062]

[0063] This means that the cell needs additional cooling relative to the average temperature, with a difference of 5°C. This difference will decrease as the cooling fluid properties are changed (for example, using a fluid with a higher heat capacity coefficient).

[0064] Assuming there are 4 cells, through the above calculation method, four sets of data are obtained: 4.5℃, -0.5℃, 1.5℃, and -5.5℃. The cooling demand parameter matrix C can be expressed as:

[0065]

[0066] Step S40: According to the cooling demand parameter matrix C, the flow rate of the coolant is dynamically adjusted by the particle swarm optimization algorithm. The optimization goal is to control the temperature difference of each battery cell within ±2°C, the flow rate range of the coolant is 50 to 200 liters per minute, and the flow rate adjustment range is 1 to 5 m / s;

[0067] It should be noted that the target of controlling the temperature difference within ±2°C helps to ensure the temperature consistency of all cells in the battery pack and avoid battery performance degradation or safety issues caused by local overheating or overcooling. Through the particle swarm optimization algorithm, the flow rate and flow velocity can be adjusted intelligently, so that the coolant can be distributed to the required area more accurately.

[0068] It should be understood that in order to achieve better cooling effect, the present invention combines the dynamic adjustment capability of the particle swarm optimization algorithm. Specifically, in the initial state, the system will quickly find the optimal solution for the flow rate through the global search function of the algorithm, and make subtle adjustments according to the cooling requirements during operation. Compared with the traditional fixed flow rate control method, this dynamic adjustment method can respond to the real-time needs of the system more efficiently and further improve the cooling efficiency.

[0069] For example, suppose that in a certain battery pack, the temperature of cells 1 and 3 is higher, and the temperature of cells 2 and 4 is lower. The system initializes 10 particles, the coolant flow rate range is 1 to 5 m / s, and the flow rate range is 50 to 200 liters per minute. After multiple iterations of the particle swarm optimization algorithm, it is finally determined that the optimal coolant flow rate is 4 m / s and the flow rate is 150 liters per minute. After the 10th iteration, the particle swarm found the global optimal solution, which reduced the temperature of cells 1 and 3, while the temperature difference between cells 2 and 4 was controlled within the target range, and the cooling system achieved an optimization effect.

[0070] Step S50: Monitor the deviation between the temperature data after cooling and the optimization target in real time through closed-loop feedback. When the temperature difference exceeds 1°C, automatically adjust the flow rate of the coolant.

[0071] It should be noted that the role of the closed-loop feedback system is to continuously monitor the operating status of the battery, especially when the battery load is high, so that it can respond to temperature changes and adjust the cooling strategy in a timely manner. Through real-time temperature monitoring and rapid response, the system can always keep the battery cell temperature within a safe range to prevent overheating or insufficient cooling of the battery cell.

[0072] It should be understood that, unlike the traditional static cooling method, the closed-loop feedback control can achieve dynamic adjustment. In the traditional cooling method, the coolant flow rate is usually fixed and cannot be flexibly adjusted according to the real-time status of the battery. In the present invention, the feedback system can not only increase the coolant flow rate when the battery is overheated, but also reduce the flow rate when the temperature returns to normal, thereby saving energy consumption and extending the system life.

[0073] For example, suppose that under a certain operating state, the temperature of battery cell 1 rises from the target temperature of 45°C to 46.5°C due to high load, exceeding the target temperature difference threshold of 1°C. At this time, the system detects that the temperature difference deviation is 1.5°C through the feedback mechanism, and immediately increases the flow rate of the coolant, so that the battery cell temperature quickly drops back to within 45°C. The flow rate adjustment formula can be implemented through an adaptive adjustment coefficient to ensure that the adjustment process is fast and stable. Through the closed-loop feedback system, the cooling system can intelligently and efficiently manage the temperature changes of the battery cells, ensuring that the lithium battery system can maintain a stable temperature range under different working conditions.

[0074] In the second embodiment, in step S40, the particle swarm optimization algorithm optimizes the target using the formula:

[0075]

[0076] Among them, T i is the temperature of the ith cell, T avg is the average temperature of the battery system, and n is the total number of battery cells.

[0077] It should be noted that the core purpose of this objective function is to minimize the difference between the battery cell temperature and the system average temperature. i With the system average temperature T avg The temperature fluctuation of all cells is obtained by summing the squares of the differences. The goal is to make this sum as small as possible, that is, to make the temperature of all cells as close to the average temperature of the system as possible to achieve uniform thermal management.

[0078] It is understood that n in the formula represents the number of all cells in the battery system. As the number of battery cells n increases, the formula will take into account the temperature changes of more cells. The temperature fluctuations of each cell will affect the cooling requirements of the entire system, so the optimization algorithm can ensure that the temperature of all cells is as consistent as possible to avoid affecting the overall performance due to overheating or insufficient cooling of a single cell.

[0079] It should be understood that the optimization goal of this formula is to gradually adjust the flow rate and flow rate of the coolant through the particle swarm algorithm to ensure that the temperature of each battery cell is at a minimum difference from the average temperature. By minimizing the value of this objective function, the system can achieve the optimal temperature distribution, thereby improving the safety and service life of the lithium battery system.

[0080] For example, assume that a lithium battery pack contains 4 cells, and the current cell temperatures are T1 = 45°C, T2 = 50°C, T3 = 47°C, and T4 = 43°C. At this time, the average temperature of the battery system is:

[0081]

[0082] Substituting these temperatures into the objective function: f(x) = (45-46.25) 2 +(50-46.25) 2 +(47-46.25) 2 +(43-46.25) 2 =26.75

[0083] Through this objective function, the cooling system can accurately control the temperature changes of each battery cell, so that the entire battery pack maintains a stable temperature during operation and optimizes system performance and life.

[0084] In addition, the present invention also provides a lithium battery thermal management device, which adopts a lithium battery thermal management control method in the above embodiment to solve a technical problem of lithium battery thermal management. Compared with the prior art, the beneficial effects of a lithium battery thermal management device provided by the present invention are the same as the beneficial effects of a lithium battery thermal management control method provided by the above embodiment, and other technical features in the lithium battery thermal management device are the same as the features disclosed in the above embodiment method, which will not be repeated here.

[0085] The present invention also provides a lithium battery thermal management device, please refer to Figure 2 , a lithium battery thermal management device includes: at least one processor; and a memory connected to the at least one processor in communication; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute a lithium battery thermal management control method in the above-mentioned embodiment 1. A lithium battery thermal management device in the embodiment of the present invention may include but is not limited to mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Descriptions), PMPs (Portable Media Players), vehicle-mounted terminals (such as vehicle-mounted navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc. A lithium battery thermal management device is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present invention. A lithium battery thermal management device may include a processing device 1001 (such as a central processing unit, a graphics processor, etc.), which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM: Read Only Memory) 1002 or a program loaded from a storage device 1003 to a random access memory (RAM: Random Access Memory) 1004. In RAM1004, various programs and data required for the operation of a lithium battery thermal management device are also stored. The processing device 1001, ROM1002 and RAM1004 are connected to each other through a bus 1005. An input / output (I / O) interface 1006 is also connected to the bus. Generally, the following systems can be connected to the I / O interface 1006: an input device 1007 including, for example, a touch screen, a touchpad, a keyboard, a mouse, an image sensor, a microphone, an accelerometer, a gyroscope, etc.; an output device 1008 including, for example, a liquid crystal display (LCD: Liquid Crystal Display), a speaker, a vibrator, etc.; a storage device 1003 including, for example, a magnetic tape, a hard disk, etc.; and a communication device 1009. The communication device 1009 can allow a lithium battery thermal management device to communicate wirelessly or wired with other devices to exchange data. Although a lithium battery thermal management device with various systems is shown in the figure, it should be understood that it is not required to implement or have all the systems shown. More or fewer systems may be implemented or have alternatively.

[0086] The present invention also provides a computer program product, including a computer program, which, when executed by a processor, implements the steps of a lithium battery thermal management control method as described above. The computer program product provided by the present invention can solve a technical problem of lithium battery thermal management. Compared with the prior art, the beneficial effects of the computer program product provided by the present invention are the same as the beneficial effects of a lithium battery thermal management control method provided by the above embodiment, which will not be repeated here.

[0087] In particular, according to the embodiments disclosed in the present invention, the process described above with reference to the flowchart can be implemented as a computer software program. For example, the embodiments disclosed in the present invention include a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program contains program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from the network through a communication device, or installed from the storage device 1003, or installed from the ROM 1002. When the computer program is executed by the processing device 1001, the above-mentioned functions defined in the method of the embodiment disclosed in the present invention are executed.

[0088] It should be understood that the various parts disclosed in the present invention can be implemented by hardware, software, firmware or a combination thereof. In the description of the above embodiments, specific features, structures, materials or characteristics can be combined in any one or more embodiments or examples in a suitable manner.

[0089] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A lithium battery thermal management control method, characterized in that: The lithium battery thermal management control method comprises: Step S10: Based on multiple embedded temperature sensors, the temperature data of the lithium battery cells are collected in real time to form a temperature matrix T. The accuracy of the temperature sensors is ±0.1°C, and the temperature collection frequency is 10 times per second. Step S20: According to the temperature matrix T, the heat flux density q between the cells is calculated by the Fourier heat conduction equation, and then the heat flux matrix H is generated, wherein the calculation formula of the heat flux density q is: Where q is the heat flux density, k is the thermal conductivity of the lithium battery material, is the temperature gradient; Step S30: Based on the heat flow matrix H, the cooling requirement of each battery cell is calculated using a differential equation to form a cooling requirement parameter matrix C. The differential equation is: Where T i is the temperature of the ith cell, T avg is the average temperature of the battery pack, R c is the heat capacity coefficient of the cooling fluid; Step S40: According to the cooling demand parameter matrix C, the flow rate of the coolant is dynamically adjusted by the particle swarm optimization algorithm. The optimization goal is to control the temperature difference of each battery cell within ±2°C, the flow rate range of the coolant is 50 to 200 liters per minute, and the flow rate adjustment range is 1 to 5 m / s; Step S50: Monitor the deviation between the temperature data after cooling and the optimization target in real time through closed-loop feedback. When the temperature difference exceeds 1°C, automatically adjust the flow rate of the coolant.

2. A lithium battery thermal management control method as claimed in claim 1, characterized in that: In step S40, the particle swarm optimization algorithm adopts the following formula: v i (t+1)=wv i (t)+c1r1(p i,best -x i (t))+c2r2(g best -x i (t)) Among them, v i (t+1) represents the velocity of particle i at time t+1, v i (t) represents the velocity of particle i at time t, x i (t) is the current position of particle i, p i,best is the historical optimal position of particle i, g best is the global optimal position, c1 and c2 are learning factors, r1 and r2 are random numbers, and w is the inertia weight.

3. A lithium battery thermal management control method as claimed in claim 1, characterized in that: In step S40, the particle swarm optimization algorithm optimizes the target using the formula: Where n is the total number of battery cells.

4. A lithium battery thermal management control method as claimed in claim 1, characterized in that: In step S50, the flow rate of the coolant is automatically adjusted using the formula: V cooland (t+1)=V cooland (t)+α·Δm Where V cooland (t+1) is the coolant flow rate at time t+1, V cooland (t) is the coolant flow rate at the current time t, α is the adaptive adjustment coefficient, which determines the amplitude of the flow rate adjustment, and Δm is the deviation between the current battery temperature and the target temperature.

5. A lithium battery thermal management control method as claimed in claim 1, characterized in that: In step S10, the embedded temperature sensor is a digital temperature sensor.

6. A lithium battery thermal management control method as claimed in claim 1, characterized in that: In step S10, the temperature collection range is -20°C to 80°C.

7. A lithium battery thermal management control method as claimed in claim 1, characterized in that: In step S50, the response time of the closed-loop feedback is less than 3 seconds.

8. A lithium battery thermal management device, characterized in that: The device includes: A temperature acquisition module is used to collect temperature data of lithium battery cells in real time based on multiple embedded temperature sensors to form a temperature matrix T. The accuracy of the temperature sensor is ±0.1°C, and the temperature acquisition frequency is 10 times per second; The heat flow calculation module is used to calculate the heat flow density q between the cells according to the temperature matrix T through the Fourier heat conduction equation, and then generate the heat flow matrix H; A cooling demand calculation module is used to calculate the cooling demand of each battery cell based on the heat flow matrix H using a differential equation to form a cooling demand parameter matrix C; The cooling flow rate adjustment module is used to dynamically adjust the flow rate of the coolant according to the cooling demand parameter matrix C through the particle swarm optimization algorithm. The optimization goal is to control the temperature difference of each battery cell within ±2°C. The flow rate range of the coolant is 50 to 200 liters per minute, and the flow rate adjustment range is 1 to 5 meters per second. The feedback control module is used to monitor the deviation between the temperature data after cooling and the optimization target in real time through closed-loop feedback. When the temperature difference exceeds 1°C, the flow rate of the coolant is automatically adjusted.

9. A lithium battery thermal management device, characterized in that the device include: A memory, a processor, and a lithium battery thermal management program stored in the memory and executable on the processor, wherein the lithium battery thermal management program implements a lithium battery thermal management control method when executed by the processor.

10. A computer program product, characterized in that The computer program product includes a lithium battery thermal management program, and when the lithium battery thermal management program is executed by a processor, a lithium battery thermal management control method is implemented.

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